Abstract

A major part of natural language processing now depends on the use of
text data to build linguistic analyzers. We consider statistical,
computational approaches to modeling linguistic structure. We seek to
unify across many approaches and many kinds of linguistic
structures. Assuming a basic understanding of natural language
processing and/or machine learning, we seek to bridge the gap between
the two fields. Approaches to decoding (i.e., carrying out linguistic
structure prediction) and supervised and unsupervised learning of
models that predict discrete structures as outputs are the focus. We
also survey natural language processing problems to which these
methods are being applied, and we address related topics in
probabilistic inference, optimization, and experimental methodology.